Cloud-enabled Al and machine learning are providing healthcare
stakeholders with the tools needed for a faster and smarter
approach to combatting the COVID-19 virus.
(WrightStudios/Shutterstock)
As COVID-19 began spreading across the U.S., healthcare
organizations were forced to quickly reassess their technology, and
pull future plans for digital transformation forward.
In record time, many organizations overhauled legacy systems to
better manage and care for the uptick in patient visits, while
safely storing data to ensure efficiency as the pandemic
evolved.
One of the most pressing priorities for healthcare organizations
was expediting their adoption of cloud technologies to more
efficiently manage the deluge of patient information, ensure
streamlined workplace practices and enable information sharing with
greater ease. As local leaders made decisions about how to keep
their populations safe, cloud infrastructure provided the ability
to collect, analyze, and share data securely across and among a
global network of organizations.
Through this period of rapid cloud adoption, there has also been
a swift uptick in the use of artificial intelligence (AI) and
machine learning technologies. From enabling information sharing
and analysis without sacrificing data privacy, to ensuring patients
with the most urgent needs are given the quickest response, these
technologies have revolutionized the COVID-19 healthcare response
and will remain critical well beyond the pandemic.
Here are just a few of the ways in which COVID-19 has spurred
lasting digital transformation within the healthcare industry:
De-identification of patient data
With machine learning capabilities, healthcare organizations are
better equipped to ensure the privacy of patient data, making it
easier to aggregate data across multiple sources and garner helpful
insights about the COVID-19 virus. De-identification, the process
of removing identifying information from patient data, is critical
to the sharing of health information with non-privileged parties
for research purposes, the creation of datasets from multiple
sources for analysis, and anonymizing data so it can be used in
advanced analytics and machine learning models.
As an example, the Google Cloud Healthcare API can detect
sensitive data, such as protected health information (PHI), and
mask, delete, or otherwise obscure it.
To enable researchers to study critical COVID-19 information for
fighting the virus, patient identities from DICOM assets, such as
lung x-rays, can be removed at scale using the same type of machine
learning technology that scans YouTube for copyright infringement,
making the data usable for analytics in high-definition. Further,
testing data can be de-identified, accelerating discovery. When
properly hashed, such data can then be safely re-identified
allowing researchers to more effectively recruit for public health
programs like clinical trials.
Natural language processing for call center responses
All types of public health organizations today are inundated
with more patient requests than ever beforeand many were not
initially equipped to manage this increase.
With cloud-based AI and machine learning models, however,
organizations can build the call center of the future. Using
natural language processing and sentiment analysis, healthcare
providers can automatically prioritize calls based on need.
This technology allows an organization to optimize its approach
to answering/prioritizing inquiries based on everything from the
distress of the voice to the age of the voice. And while theyâre
smart, many of these APIs are engineered with privacy in mind. They
donât store private data, helping ensure patient
confidentiality.
Supply chain decisions informed by predictive analytics
Cloud isnât just supporting healthcare organizations through
research and treatment decisions. It is also helping them get ahead
of supply shortages at a time when equipment is more critical to
survival than ever before.
As organizations look to provide critical healthcare equipment
such as PPE and ventilators to those in need, cloudâs predictive
analytics can help those managing the supply chain better
understand where shortages exist, and where they will soon be, in
order to allocate before there is an issue.
Matching algorithms are easily implemented alongside predictive
services to reduce waste in the supply chain, enabling real-time
visibility to both suppliers and procurers.
Cloud-enabled Al and machine learning are providing healthcare
stakeholders with the tools needed for a faster and smarter
approach to combatting the COVID-19 virus. While the mission today
is singular, this technology, along with the innovative ideas
coming from our nationâs top minds, will change the face of
healthcare as we know it, allowing for a greater patient experience
than ever before.
Originally published by
Lisa
Noon, Deloitte | Nov 23, 2020
Fierce
Healthcare